InĀ [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PlateAnalysis_Echo import PlateDataset
from tqdm import tqdm
dataset_0 = PlateDataset('20191105_Dopt_2.CSV','D_Opt.csv')
dataset_1 = PlateDataset('20191105_Dopt_2_Shaking.CSV','D_Opt.csv')
dataset_2 = PlateDataset('20191105_Dopt_2_Shaking_rep2.CSV','D_Opt.csv')
dataset_3 = PlateDataset('20191105_Dopt_2_Shaking_rep3.CSV','D_Opt.csv')
output = pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])
for i in tqdm(range(1,19)):
output=output.append(dataset_1.CalculateMetrics(i))
output=output.append(dataset_2.CalculateMetrics(i))
output=output.append(dataset_3.CalculateMetrics(i))
output = output.reset_index(drop=True)
output.to_csv('DOptExperimnentResults.csv')
output
100%|āāāāāāāāāā| 18/18 [00:42<00:00, 2.29s/it]
Out[1]:
| K | percentDMSO | Vol | km | vmax | loss | r_squared | |
|---|---|---|---|---|---|---|---|
| 0 | 1.000000 | 5.000000 | 20.0 | 2.675369 | 0.047003 | 0.012980 | 0.328103 |
| 1 | 1.000000 | 5.000000 | 20.0 | 2.227608 | 0.027246 | 0.007528 | 0.517799 |
| 2 | 1.000000 | 5.000000 | 20.0 | 2.581205 | 0.045789 | 0.015981 | 0.368032 |
| 3 | 2.636364 | 3.181818 | 20.0 | 2.475195 | 0.031945 | 0.005781 | 0.691983 |
| 4 | 2.636364 | 3.181818 | 20.0 | 2.109391 | 0.039754 | 0.003395 | 0.885325 |
| 5 | 2.636364 | 3.181818 | 20.0 | 2.036853 | 0.033326 | 0.007022 | 0.676865 |
| 6 | 4.000000 | 1.000000 | 20.0 | 0.977021 | 0.058728 | 0.001813 | 0.990724 |
| 7 | 4.000000 | 1.000000 | 20.0 | 2.257035 | 0.061204 | 0.007720 | 0.816803 |
| 8 | 4.000000 | 1.000000 | 20.0 | 0.484916 | 0.048700 | 0.001620 | 0.986696 |
| 9 | 4.000000 | 1.000000 | 20.0 | 2.547141 | 0.052887 | 0.003677 | 0.935980 |
| 10 | 4.000000 | 1.000000 | 20.0 | 1.865010 | 0.057868 | 0.009088 | 0.471061 |
| 11 | 4.000000 | 1.000000 | 20.0 | 0.125037 | 0.048281 | 0.006071 | 0.768513 |
| 12 | 1.545455 | 1.000000 | 20.0 | 2.483997 | 0.036234 | 0.012994 | 0.312070 |
| 13 | 1.545455 | 1.000000 | 20.0 | 2.713912 | 0.050843 | 0.015150 | 0.252104 |
| 14 | 1.545455 | 1.000000 | 20.0 | 2.740432 | 0.054430 | 0.015246 | 0.315840 |
| 15 | 1.000000 | 5.000000 | 20.0 | 1.932250 | 0.010229 | 0.012775 | -0.077052 |
| 16 | 1.000000 | 5.000000 | 20.0 | 2.180274 | 0.022834 | 0.009380 | 0.284250 |
| 17 | 1.000000 | 5.000000 | 20.0 | 1.984917 | 0.016148 | 0.010116 | 0.165649 |
| 18 | 2.636364 | 3.181818 | 20.0 | -1.569767 | 0.049392 | 0.004332 | 0.998290 |
| 19 | 2.636364 | 3.181818 | 20.0 | -0.683843 | 0.049637 | 0.031566 | 0.160381 |
| 20 | 2.636364 | 3.181818 | 20.0 | -0.300910 | 0.051674 | 0.026682 | 0.079612 |
| 21 | 2.636364 | 5.000000 | 29.0 | 2.824726 | 0.048840 | 0.003134 | 0.885150 |
| 22 | 2.636364 | 5.000000 | 29.0 | 2.779152 | 0.050375 | 0.011771 | 0.335623 |
| 23 | 2.636364 | 5.000000 | 29.0 | 2.352918 | 0.047634 | 0.009516 | 0.314454 |
| 24 | 2.636364 | 5.000000 | 29.0 | 3.372817 | 0.074971 | 0.007911 | 0.837700 |
| 25 | 2.636364 | 5.000000 | 29.0 | 3.093418 | 0.073908 | 0.010997 | 0.765327 |
| 26 | 2.636364 | 5.000000 | 29.0 | 3.089719 | 0.073567 | 0.009030 | 0.745127 |
| 27 | 1.000000 | 2.818182 | 29.0 | 2.759980 | 0.053463 | 0.018398 | 0.117035 |
| 28 | 1.000000 | 2.818182 | 29.0 | 2.895809 | 0.066171 | 0.020328 | -0.085152 |
| 29 | 1.000000 | 2.818182 | 29.0 | 3.016501 | 0.066801 | 0.020774 | -0.128463 |
| 30 | 1.000000 | 2.818182 | 29.0 | 2.403532 | 0.077447 | 0.002035 | 0.972183 |
| 31 | 1.000000 | 2.818182 | 29.0 | 2.438965 | 0.079011 | 0.001350 | 0.988732 |
| 32 | 1.000000 | 2.818182 | 29.0 | 2.441895 | 0.078780 | 0.002889 | 0.967037 |
| 33 | 1.000000 | 5.000000 | 40.0 | 2.911388 | 0.071689 | 0.009754 | 0.382532 |
| 34 | 1.000000 | 5.000000 | 40.0 | 3.082599 | 0.069612 | 0.010115 | 0.392385 |
| 35 | 1.000000 | 5.000000 | 40.0 | 3.094629 | 0.069797 | 0.011311 | 0.333080 |
| 36 | 1.000000 | 1.000000 | 40.0 | 1.468051 | 0.115580 | 0.004583 | 0.977210 |
| 37 | 1.000000 | 1.000000 | 40.0 | 3.741961 | 0.126283 | 0.002852 | 0.991068 |
| 38 | 1.000000 | 1.000000 | 40.0 | 3.486001 | 0.119758 | 0.002842 | 0.988892 |
| 39 | 4.000000 | 5.000000 | 40.0 | 1.446555 | 0.110717 | 0.004600 | 0.978295 |
| 40 | 4.000000 | 5.000000 | 40.0 | 1.523311 | 0.108327 | 0.004352 | 0.982013 |
| 41 | 4.000000 | 5.000000 | 40.0 | 1.123542 | 0.098365 | 0.003310 | 0.987439 |
| 42 | 1.000000 | 5.000000 | 40.0 | 3.162315 | 0.075010 | 0.027382 | 0.089503 |
| 43 | 1.000000 | 5.000000 | 40.0 | 3.152601 | 0.072772 | 0.024214 | 0.037659 |
| 44 | 1.000000 | 5.000000 | 40.0 | 3.102813 | 0.070091 | 0.027736 | 0.106447 |
| 45 | 1.000000 | 1.000000 | 40.0 | 2.310959 | 0.031861 | 0.027076 | 0.113705 |
| 46 | 1.000000 | 1.000000 | 40.0 | 2.298626 | 0.031986 | 0.022425 | 0.171065 |
| 47 | 1.000000 | 1.000000 | 40.0 | 2.260342 | 0.028294 | 0.024612 | 0.071522 |
| 48 | 4.000000 | 1.000000 | 40.0 | 0.035702 | 0.110071 | 0.004118 | 0.972540 |
| 49 | 4.000000 | 1.000000 | 40.0 | 0.024896 | 0.108735 | 0.003161 | 0.974311 |
| 50 | 4.000000 | 1.000000 | 40.0 | 0.180984 | 0.099658 | 0.005794 | 0.873542 |
| 51 | 4.000000 | 1.000000 | 40.0 | 0.970282 | 0.103618 | 0.003518 | 0.980284 |
| 52 | 4.000000 | 1.000000 | 40.0 | 0.448479 | 0.091980 | 0.003289 | 0.987029 |
| 53 | 4.000000 | 1.000000 | 40.0 | 0.963389 | 0.099711 | 0.003788 | 0.983885 |
InĀ [2]:
Rep1_NoShake = pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])
Rep1_Shake = pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])
for i in tqdm(range(1,19)):
Rep1_NoShake=Rep1_NoShake.append(dataset_0.CalculateMetrics(i))
Rep1_Shake=Rep1_Shake.append(dataset_1.CalculateMetrics(i))
Rep1_NoShake.reset_index(inplace=True,drop=True)
Rep1_Shake.reset_index(inplace=True,drop=True)
MixingDifference = Rep1_Shake['r_squared'] - Rep1_NoShake['r_squared']
MixingDifference.name = 'Difference in R sq'
pd.concat([Rep1_Shake[['K','percentDMSO','Vol']],MixingDifference],axis=1)
100%|āāāāāāāāāā| 18/18 [00:26<00:00, 1.56s/it]
Out[2]:
| K | percentDMSO | Vol | Difference in R sq | |
|---|---|---|---|---|
| 0 | 1.000000 | 5.000000 | 20.0 | 0.068145 |
| 1 | 2.636364 | 3.181818 | 20.0 | 0.229469 |
| 2 | 4.000000 | 1.000000 | 20.0 | 0.063834 |
| 3 | 4.000000 | 1.000000 | 20.0 | 0.034579 |
| 4 | 1.545455 | 1.000000 | 20.0 | 0.056007 |
| 5 | 1.000000 | 5.000000 | 20.0 | -0.032856 |
| 6 | 2.636364 | 3.181818 | 20.0 | 1.009708 |
| 7 | 2.636364 | 5.000000 | 29.0 | -0.001880 |
| 8 | 2.636364 | 5.000000 | 29.0 | 0.053933 |
| 9 | 1.000000 | 2.818182 | 29.0 | -0.083629 |
| 10 | 1.000000 | 2.818182 | 29.0 | -0.001108 |
| 11 | 1.000000 | 5.000000 | 40.0 | -0.017544 |
| 12 | 1.000000 | 1.000000 | 40.0 | -0.001960 |
| 13 | 4.000000 | 5.000000 | 40.0 | -0.003488 |
| 14 | 1.000000 | 5.000000 | 40.0 | -0.012591 |
| 15 | 1.000000 | 1.000000 | 40.0 | -0.004671 |
| 16 | 4.000000 | 1.000000 | 40.0 | 0.007028 |
| 17 | 4.000000 | 1.000000 | 40.0 | -0.003935 |
InĀ [2]:
for i in range(1,19):
print('Rep 1')
dataset_1.PlotFigure(i)
print('Rep 2')
dataset_2.PlotFigure(i)
print('Rep 3')
dataset_3.PlotFigure(i)
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InĀ [Ā ]:
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
decomp = pca.fit_transform(dataset_3.data)
plt.scatter(decomp[:,0],decomp[:,1])
plt.show()
InĀ [13]:
plt.figure(figsize=(20,5))
for i in dataset_1.data.index:
plt.plot(dataset_1.data.loc[i,:],alpha = 0.2)
plt.xticks(range(220,800,10))
plt.ylim((0,0.5))
plt.xlim(300,800)
plt.show()
InĀ [6]:
dataset_1
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-6-7f6531e28fd8> in <module> ----> 1 dataset_1.Diff(data) AttributeError: 'PlateDataset' object has no attribute 'Diff'